Abstract
In this paper, we propose a subMarkov random walk (subRW) with the label prior with added auxiliary nodes for seeded image segmentation. We unify the proposed subRW and the other popular random walk algorithms. This unifying view can transfer the intrinsic findings between different random walk algorithms, and offer the new ideas for designing the novel random walk algorithms by changing the auxiliary nodes. According to the second benefit, we design a subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on natural images with twigs demonstrate that our algorithm achieves better performance than the previous random walk algorithms.
This work was supported in part by the National Basic Research Program of China (973 Program) (No. 2013CB328805), the Key Program of NSFC-Guangdong Union Foundation (No. U1035004), the National Natural Science Foundation of China (No. 61272359), and the Program for New Century Excellent Talents in University (NCET-11-0789). Beijing Higher Education Young Elite Teacher Project. Specialized Fund for Joint Building Program of Beijing Municipal Education Commission.
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Dong, X., Shen, J., Van Gool, L. (2015). Segmentation Using SubMarkov Random Walk. In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, Cham. https://doi.org/10.1007/978-3-319-14612-6_18
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DOI: https://doi.org/10.1007/978-3-319-14612-6_18
Publisher Name: Springer, Cham
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